class_docstr: DropFeatures() drops a list of variables indicated by the user from the dataframe.
  
  More details in the :ref:`User Guide <drop_features>`.
  
  Parameters
  ----------
  features_to_drop: str or list
      Variable(s) to be dropped from the dataframe
  
  Attributes
  ----------
  features_to_drop_:
      The features that will be dropped.
  
  feature_names_in_:
      List with the names of features seen during `fit`.
  
  n_features_in_:
      The number of features in the train set used in fit.
  
  Methods
  -------
  fit:
      This transformer does not learn any parameter.
  
  fit_transform:
      Fit to data, then transform it.
  
  get_feature_names_out:
      Get output feature names for transformation.
  
  get_support:
      Get a mask, or integer index, of the features selected.
  
  get_params:
      Get parameters for this estimator.
  
  set_params:
      Set the parameters of this estimator.
  
  transform:
      Drops indicated features.
docstr: This transformer does not learn any parameter.
  
  Parameters
  ----------
  X : pandas dataframe of shape = [n_samples, n_features]
      The input dataframe
  y : pandas Series, default = None
      y is not needed for this transformer. You can pass y or None.
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code: def fit(self, X: pd.DataFrame, y: pd.Series = None):
      """
      This transformer does not learn any parameter.
  
      Parameters
      ----------
      X : pandas dataframe of shape = [n_samples, n_features]
          The input dataframe
      y : pandas Series, default = None
          y is not needed for this transformer. You can pass y or None.
      """
      # check input dataframe
      X = check_X(X)
  
      # X[self.features_to_drops] calls to pandas to check if columns are
      # present in the df.
      X[self.features_to_drop]
  
      self.features_to_drop_ = self.features_to_drop
  
      # check user is not removing all columns in the dataframe
      if len(self.features_to_drop) == len(X.columns):
          raise ValueError(
              "The resulting dataframe will have no columns after dropping all "
              "existing variables"
          )
  
      # save input features
      self._get_feature_names_in(X)
  
      return self
  
file_path: feature_engine/feature_engine/selection/drop_features.py
imports: from typing import List, Union
  import pandas
  from feature_engine.dataframe_checks import check_X
  from feature_engine.selection.base_selector import BaseSelector
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 2 for calling the DropFeatures obj's underlying member method code with example usage: obj.fit(X, y) and returns: self